In Silico Evaluation of Bilinear Elastoplastic Coronary Artery Stents
Bibliographic record
Abstract
Mechanical responses of the endovascular stent determine the arterial homeostasis and vulnerability of the atherosclerotic plaque. Given the various plaque components when the stent is deployed, the stent may apply excessive stress to the lesion and cause plaque rupture. Herein, using the interaction between the Palmaz–Schatz stent with two stent biomaterials, stainless steel, and magnesium alloy, and three different types of plaque, namely hypocellular, hypercellular, and calcified, are studied. An implicit finite element method is used to simulate and analyze the stress and strain acting on the stents, artery, and plaques. The Mooney–Rivlin hyperelastic material model is considered to study the responses of each component. The results reveal that stainless‐steel stents applied a higher level of stress to the plaques and vessel wall, which may lead to vascular damage and plaque rupture. However, a magnesium alloy stent with the similar design and geometrical parameters generates less stress on the plaque and artery. Interestingly, a minor improvement in magnesium alloy stents, increasing the strut thickness, can enhance the stent performance and lower the applied stresses to the vasculature and plaque, making them an ideal choice of material for stenting applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".